v2: add English originals; LLM-review and correct all translations; recover examples dropped by original ChineseSquad; add audit logs

.gitattributes

@@ -56,3 +56,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+corrections/train.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,16 +1,18 @@
 ---
 annotations_creators:
 - machine-translated
+- machine-generated
 language_creators:
 - machine-translated
 language:
 - zh
+- en
 license:
 - cc-by-sa-4.0
 multilinguality:
-- monolingual
+- translation
 size_categories:
-- 10K<n<100K
+- 100K<n<1M
 source_datasets:
 - squad_v2
 task_categories:
@@ -18,128 +20,199 @@ task_categories:
 task_ids:
 - open-domain-qa
 - extractive-qa
-pretty_name: Chinese SQuAD 2.0
+pretty_name: Chinese SQuAD 2.0 (revised, with English originals)
+dataset_info:
+  config_name: chinese_squadv2
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: context
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answers
+    struct:
+    - name: text
+      sequence: string
+    - name: answer_start
+      sequence: int64
+  - name: title_en
+    dtype: string
+  - name: context_en
+    dtype: string
+  - name: question_en
+    dtype: string
+  - name: answers_en
+    struct:
+    - name: text
+      sequence: string
+    - name: answer_start
+      sequence: int64
+  - name: is_impossible
+    dtype: bool
+  - name: translation_source
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 130162
+  - name: validation
+    num_examples: 11856
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
 ---
 
-# Dataset Card for Chinese SQuAD 2.0
+# Dataset Card for Chinese SQuAD 2.0 (revised, bilingual)
 
 ## Dataset Description
 
-### Dataset Summary
-
-This is a Chinese translation of the SQuAD 2.0 dataset, translated from the original English version. Like SQuAD 2.0, it contains both answerable and unanswerable questions. The dataset is designed for Chinese reading comprehension and question answering tasks.
-
-Source: [ChineseSquad](https://github.com/junzeng-pluto/ChineseSquad)
+This is a revised and extended version of the Chinese translation of SQuAD 2.0,
+originally machine-translated by
+[ChineseSquad](https://github.com/junzeng-pluto/ChineseSquad). Like SQuAD 2.0 it
+contains both answerable and unanswerable questions and is designed for Chinese
+extractive reading comprehension / question answering.
+
+Compared with the previous release of `chinese-squadv2`, this version:
+
+1. **Adds the original English SQuAD 2.0 fields** (`title_en`, `context_en`,
+   `question_en`, `answers_en`, `is_impossible`), aligned to every example by the
+   original SQuAD id. For the validation split, `answers_en` restores **all**
+   human reference answers from the official dev set (deduplicated), not just one.
+2. **Reviews and corrects the Chinese machine translation** of all
+   99,963 pre-existing examples. Every unique paragraph
+   together with its questions and answers was checked against the English original
+   by LLMs (grok-3-mini-fast: 9,797 paragraph groups, gemini-3-flash: 9,731,
+   qwen3.6-flash: 3, kimi-k2: 1) with a constrained-correction protocol:
+   corrections were accepted only if every Chinese answer remains a verbatim
+   substring of the (possibly corrected) Chinese context, and `answer_start`
+   offsets were recomputed programmatically. Typical fixed errors: mistranslated
+   terminology (e.g. 教会-图灵论点 -> 丘奇-图灵论题, 胶带 -> 纸带),
+   wrong senses (维多利亚时代 -> 维多利亚州), untranslated fragments (张R -> 张柔),
+   and garbled sentences.
+3. **Recovers 42,055 of the 42,229 examples that the
+   original ChineseSquad project dropped** (answerable questions whose answer spans
+   could not be aligned after 2019-era machine translation). They were re-translated
+   with the same LLM pool under the same substring constraint: where the paragraph
+   already had a (corrected) Chinese context, only the question and answer span were
+   translated against that fixed context; 174
+   examples whose answers still could not be aligned remain excluded and are listed
+   in `corrections/unrecovered_*.jsonl`.
+4. **Ships a full audit log** (`corrections/`) listing every changed example with
+   old/new text and the model that produced it, so all edits can be reviewed.
 
 ### Dataset Structure
 
-The dataset is stored in Parquet format and contains the following fields:
-
 ```python
 {
-    'id': string,
-    'title': string,
-    'context': string,
-    'question': string,
-    'answers': {
-        'text': List[string],
-        'answer_start': List[int]
-    }
+    'id': string,                 # original SQuAD 2.0 id
+    'title': string,              # Chinese article title
+    'context': string,            # Chinese paragraph (revised)
+    'question': string,           # Chinese question (revised)
+    'answers': {'text': List[string], 'answer_start': List[int]},   # in Chinese context
+    'title_en': string,           # original English title
+    'context_en': string,         # original English paragraph
+    'question_en': string,        # original English question
+    'answers_en': {'text': List[string], 'answer_start': List[int]}, # in English context
+    'is_impossible': bool,        # True = unanswerable (both answers lists empty)
+    'translation_source': string, # 'chinesesquad-revised' | 'llm-recovered'
 }
 ```
 
+Unanswerable questions have empty `answers` / `answers_en` lists, consistent with
+the `squad_v2` convention. Rows are ordered following the official English
+SQuAD 2.0 file order.
+
 ### Data Splits
 
-| Split      | Total Examples | Answerable | Unanswerable |
-|------------|---------------|------------|--------------|
-| train      | 90,027        | 46,529     | 43,498       |
-| validation | 9,936         | 3,991      | 5,945        |
+| Split      | Examples | Answerable | Unanswerable | of which recovered | Unique paragraphs |
+|------------|----------|------------|--------------|--------------------|-------------------|
+| train      | 130,162 | 86,664 | 43,498 | 40,135 | 19,029 |
+| validation | 11,856 | 5,911 | 5,945 | 1,920 | 1,204 |
+
+Coverage vs the official English SQuAD 2.0: 130,162/130,319 train and
+11,856/11,873 dev examples (157 + 17
+examples could not be recovered; see `corrections/unrecovered_*.jsonl`).
+All ids map 1:1 into the official English SQuAD 2.0.
+
+### Revision statistics (pre-existing examples)
+
+| Split | Examples changed | Context revised | Question revised | Answer text revised | Verified unchanged |
+|-------|------------------|-----------------|------------------|---------------------|--------------------|
+| train | 76,791 | 72,984 | 25,055 | 2,515 | 13,236 |
+| validation | 8,846 | 8,480 | 3,005 | 310 | 1,090 |
+
+`answer_start` offsets were recomputed for every example whose context changed.
+All 19,532 unique paragraph groups were successfully reviewed; no example was left
+unverified in this release. The span invariant (every Chinese answer appears
+verbatim at its `answer_start` in its Chinese context) holds for 100% of examples.
 
 ### Usage
 
 ```python
 from datasets import load_dataset
 
-# Load from Hugging Face Hub
 dataset = load_dataset("real-jiakai/chinese-squadv2")
 
-# Example usage
 example = dataset['train'][0]
-print(f"Question: {example['question']}")
-print(f"Context: {example['context']}")
-print(f"Answer: {example['answers']['text']}")
-```
+print(example['question'])      # Chinese question
+print(example['question_en'])   # original English question
+print(example['answers'])       # answer span in the Chinese context
+print(example['answers_en'])    # answer span(s) in the English context
 
-Example output:
-
-```python
-Question: 碧昂丝在成长过程中，在哪些领域竞争？
-Context: 碧昂丝·吉赛尔·诺尔斯·卡特（生于1981年9月4日）是美国歌手、作曲家、唱片制作人和女演员。她在得克萨斯州休斯顿出生长大，小时候参加过各种歌舞比赛，上世纪90年代末以R&B女团“命运之子”的主唱而声名鹊起。由她父亲马修·诺尔斯（Mathew Knowles）管理的这个集团，一直以来都是世界上最畅销的女孩集团之一。暂停期间，碧昂丝发行了首张专辑《恋爱中的危险》（2003），确立了她作为全球独唱艺术家的地位，获得了五项格莱美奖，并在广告牌上热播100首单曲《疯狂恋爱》和《小男孩》。
-Answer: ['歌舞']
+# only the original (revised) ChineseSquad subset:
+subset = dataset.filter(lambda e: e['translation_source'] == 'chinesesquad-revised')
 ```
 
 ### Citation
 
-If you use this dataset, please cite both the original SQuAD 2.0 paper and the Chinese translation:
+If you use this dataset, please cite the original SQuAD papers and the Chinese
+translation project:
 
 ```bibtex
 @inproceedings{rajpurkar-etal-2018-know,
     title = "Know What You Don{'}t Know: Unanswerable Questions for {SQ}u{AD}",
-    author = "Rajpurkar, Pranav  and
-      Jia, Robin  and
-      Liang, Percy",
-    editor = "Gurevych, Iryna  and
-      Miyao, Yusuke",
+    author = "Rajpurkar, Pranav and Jia, Robin and Liang, Percy",
     booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
-    month = jul,
     year = "2018",
-    address = "Melbourne, Australia",
-    publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/P18-2124",
     doi = "10.18653/v1/P18-2124",
-    pages = "784--789",
-    eprint={1806.03822},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
+    pages = "784--789"
 }
 
 @inproceedings{rajpurkar-etal-2016-squad,
     title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
-    author = "Rajpurkar, Pranav  and
-      Zhang, Jian  and
-      Lopyrev, Konstantin  and
-      Liang, Percy",
-    editor = "Su, Jian  and
-      Duh, Kevin  and
-      Carreras, Xavier",
+    author = "Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy",
     booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
-    month = nov,
     year = "2016",
-    address = "Austin, Texas",
-    publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/D16-1264",
     doi = "10.18653/v1/D16-1264",
-    pages = "2383--2392",
-    eprint={1606.05250},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL},
+    pages = "2383--2392"
 }
 
 @misc{ChineseSquad,
     title = "ChineseSquad",
     author = "junzeng-pluto",
-    url = "https://github.com/junzeng-pluto/ChineseSquad",
+    url = "https://github.com/junzeng-pluto/ChineseSquad"
 }
 ```
 
 ### License
 
-This dataset is licensed under CC BY-SA 4.0, following the original SQuAD 2.0 license.
+CC BY-SA 4.0, following the original SQuAD 2.0 license.
 
-### Limitations and Bias
+### Limitations
 
-- Current version contains ~100k examples, which is less than the original SQuAD 2.0
-- As a machine-translated dataset, some nuances from the original English text might be lost or altered
-- The answer spans are machine-aligned after translation, which might introduce some noise
-- The dataset inherits any biases present in the original SQuAD 2.0 dataset
-- Translation quality may vary across different examples
+- Translations were machine-produced and machine-reviewed; residual errors are
+  possible. The audit log allows targeted human review.
+- 174 examples of the official SQuAD 2.0 could
+  not be aligned and remain excluded (`corrections/unrecovered_*.jsonl`).
+- The Chinese `answers` field keeps a single reference answer per answerable
+  question (the English `answers_en` field carries all references for validation).
+- Recovered examples (`translation_source == 'llm-recovered'`) were translated by
+  LLMs in 2026 and did not go through the original ChineseSquad pipeline.

corrections/train.jsonl

@@ -0,0 +1,3 @@
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+oid sha256:1086aa1ee1f0984e4f34557acc45fea02a14cfa2a9a33c2622980dc6ed3ef586
+size 19696155

corrections/unrecovered_train.jsonl

@@ -0,0 +1,157 @@
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             "feature": {
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-                "answer_start": {"dtype": "int32", "_type": "Sequence"}
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+                },
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             },
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+        },
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+            "_type": "Value"
+        },
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+                }
+            },
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+        },
+        "is_impossible": {
+            "dtype": "bool",
+            "_type": "Value"
+        },
+        "translation_source": {
+            "dtype": "string",
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         }
     },
     "splits": {
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+            "num_examples": 130162
         },
         "validation": {
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+            "num_examples": 11856
         }
     }
-}
+}